The invention concerns a method of signal processing, wherein signals S are checked for belonging to objects of desired classes Zk and differentiated from objects in an undesired class ZA.
In order not to skip over any objects of relevance, segmenting algorithms produce, in general, a large number of hypotheses whose examination requires a large expenditure of time. Another disadvantage is that a segmenting algorithm can often only consider a small number of attributes of the object to be segmented, such as shape or color, in order to analyze in real time a complete picture or at least a region of a picture in which new objects can knowingly emerge. In the case of a segmentation of circular traffic signs in a street scene, the segmentation is performed, in general, via a Hough-transformation (K. R. Castelman; Digital Image Processing, Prentice Hall, New Jersey, 1996) or a distance transformation that is based upon a matching algorithm (D. M. Gavrilla; Multi-Feature-Hierarchical Template Matching Using Distance Transforms, IEEE Int. Conf. on Pattern Recognition, pp 439-444, Brisbane, 1998), all in order to find all shapes typical for a circular traffic sign.
For a classification, which follows such a segmentation, the principal problem is not the differentiation of the different objects of the class Zk (e.g. traffic signs) from each other, but rather the difficulty of differentiating the objects of this class Zk from the objects of the undesired class ZA. Therein the objects of the undesired class ZA consist of any image field which are selected by the segmentation algorithm due to their similarity to objects of the class Zk.
This leads to a two class problem, in which certainly only the class Zk is more or less locatable in the feature realm or set, while the class ZA is widely scattered over the feature set. Therein, in general, it is not possible to find a limited number of xe2x80x98typicalxe2x80x99 objects, that are associated with the class ZA. Were number of objects of the class ZA so limited, then it would be possible to generalize a classifier starting from a set of learning examples of the total variations of possible elements of the class ZA; assuming that objects are limited to those from a closed world (closed world assumption) from which the general classification theory originates is damaged in this case.
In reality, most of the objects produced by a segmentation algorithm belong to the class ZA. In the case of traffic sign recognition these are typically more than 95 percent, which further complicates the classification problem.
The task of the invention is to develop a signal processing method, which has high certainty while processing the signal in real time and avoiding a false classification of objects of the class ZA, i.e. that the probability is kept low that objects of class ZA will be falsely assigned to one of class Zk.